Merge branch 'main' into breaking

Author: penelopeysm

HISTORY.md

@@ -1,3 +1,12 @@
+# Release 0.38.3
+
+`getparams(::Model, ::AbstractVarInfo)` now returns an empty `Float64[]` if the VarInfo contains no parameters.
+
+# Release 0.38.2
+
+Bump compat for `MCMCChains` to `7`.
+By default, summary statistics and quantiles for chains are no longer printed; to access these you should use `describe(chain)`.
+
 # Release 0.38.1
 
 The method `Bijectors.bijector(::DynamicPPL.Model)` was moved to DynamicPPL.jl.

Project.toml

@@ -68,7 +68,7 @@ ForwardDiff = "0.10.3"
 Libtask = "0.8.8"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
-MCMCChains = "5, 6"
+MCMCChains = "5, 6, 7"
 NamedArrays = "0.9, 0.10"
 Optim = "1"
 Optimization = "3, 4"

src/mcmc/Inference.jl

@@ -358,6 +358,9 @@ function getparams(model::DynamicPPL.Model, vi::DynamicPPL.VarInfo)
     # Materialize the iterators and concatenate.
     return mapreduce(collect, vcat, iters)
 end
+function getparams(::DynamicPPL.Model, ::DynamicPPL.VarInfo{NamedTuple{(),Tuple{}}})
+    return float(Real)[]
+end
 
 function _params_to_array(model::DynamicPPL.Model, ts::Vector)
     names_set = OrderedSet{VarName}()

src/mcmc/mh.jl

@@ -246,6 +246,7 @@ end
     ]
     return expr
 end
+_val_tuple(::VarInfo, ::Tuple{}) = ()
 
 @generated function _dist_tuple(
     props::NamedTuple{propnames}, vi::VarInfo, vns::NamedTuple{names}
@@ -267,6 +268,7 @@ end
     ]
     return expr
 end
+_dist_tuple(::@NamedTuple{}, ::VarInfo, ::Tuple{}) = ()
 
 # Utility functions to link
 should_link(varinfo, sampler, proposal) = false

test/Project.toml

@@ -59,7 +59,7 @@ HypothesisTests = "0.11"
 LinearAlgebra = "1"
 LogDensityProblems = "2"
 LogDensityProblemsAD = "1.4"
-MCMCChains = "5, 6"
+MCMCChains = "5, 6, 7"
 Mooncake = "0.4.95"
 NamedArrays = "0.9.4, 0.10"
 Optim = "1"

test/mcmc/Inference.jl

@@ -631,6 +631,39 @@ using Turing
             StableRNG(seed), demo_incorrect_missing([missing]), NUTS(), 10; check_model=true
         )
     end
+
+    @testset "getparams" begin
+        @model function e(x=1.0)
+            return x ~ Normal()
+        end
+        evi = Turing.VarInfo(e())
+        @test isempty(Turing.Inference.getparams(e(), evi))
+
+        @model function f()
+            return x ~ Normal()
+        end
+        fvi = Turing.VarInfo(f())
+        @test only(Turing.Inference.getparams(f(), fvi)) == (@varname(x), fvi[@varname(x)])
+
+        @model function g()
+            x ~ Normal()
+            return y ~ Poisson()
+        end
+        gvi = Turing.VarInfo(g())
+        gparams = Turing.Inference.getparams(g(), gvi)
+        @test gparams[1] == (@varname(x), gvi[@varname(x)])
+        @test gparams[2] == (@varname(y), gvi[@varname(y)])
+        @test length(gparams) == 2
+    end
+
+    @testset "empty model" begin
+        @model function e(x=1.0)
+            return x ~ Normal()
+        end
+        # Can't test with HMC/NUTS because some AD backends error; see 
+        # https://github.com/JuliaDiff/DifferentiationInterface.jl/issues/802
+        @test sample(e(), IS(), 100) isa MCMCChains.Chains
+    end
 end
 
 end